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启智社区(中文版) | **OpenHGNN [CIKM2022]** | **Space4HGNN [SIGIR2022]** | Benchmark&Leaderboard | Slack Channel
This is an open-source toolkit for Heterogeneous Graph Neural Network based
on DGL [Deep Graph Library] and PyTorch. We integrate SOTA models
of heterogeneous graph.
OpenHGNN won the Excellent Incubation Program Award of OpenI Community! For more details:https://mp.weixin.qq.com/s/PpbwEdP0-8wG9dsvRvRDaA
The algorithm library supports the project of "Intelligent Analysis Technology and Scale Application of Large Scale Complex Heterogeneous Graph Data" led by BUPT and participated by ANT GROUP, China Mobile, Haizhi Technology, etc. This project won the first prize of the 2022 Chinese Intitute of Electronics "Science and Technology Progress Award".
We release the latest version v0.4.
We release the latest version v0.3.
We release the latest version v0.2.
1. Python environment (Optional): We recommend using Conda package manager
conda create -n openhgnn python=3.6
source activate openhgnn
2. Install Pytorch: Follow their tutorial to run the proper command according to
your OS and CUDA version. For example:
pip install torch torchvision torchaudio
3. Install DGL: Follow their tutorial to run the proper command according to
your OS and CUDA version. For example:
pip install dgl -f https://data.dgl.ai/wheels/repo.html
4. Install openhgnn:
pip install openhgnn
git clone https://github.com/BUPT-GAMMA/OpenHGNN
# If you encounter a network error, try git clone from openi as following.
# git clone https://git.openi.org.cn/GAMMALab/OpenHGNN.git
cd OpenHGNN
pip install .
python main.py -m model_name -d dataset_name -t task_name -g 0 --use_best_config --load_from_pretrained
usage: main.py [-h] [--model MODEL] [--task TASK] [--dataset DATASET]
[--gpu GPU] [--use_best_config]
optional arguments:
-h, --help
show this help message and exit
--model -m
name of models
--task -t
name of task
--dataset -d
name of datasets
--gpu -g
controls which gpu you will use. If you do not have gpu, set -g -1.
--use_best_config
use_best_config means you can use the best config in the dataset with the model. If you want to
set the different hyper-parameter, modify the openhgnn.config.ini manually. The best_config
will override the parameter in config.ini.
--load_from_pretrained
will load the model from a default checkpoint.
e.g.:
python main.py -m GTN -d imdb4GTN -t node_classification -g 0 --use_best_config
Note: If you are interested in some model, you can refer to the below models list.
Refer to the docs to get more basic and depth usage.
The link will give some basic usage.
Model | Node classification | Link prediction | Recommendation |
---|---|---|---|
TransE[NIPS 2013] | ✔️ | ||
TransH[AAAI 2014] | ✔️ | ||
TransR[AAAI 2015] | ✔️ | ||
TransD[ACL 2015] | ✔️ | ||
Metapath2vec[KDD 2017] | ✔️ | ||
RGCN[ESWC 2018] | ✔️ | ✔️ | |
HERec[TKDE 2018] | ✔️ | ||
HAN[WWW 2019] | ✔️ | ✔️ | |
KGCN[WWW 2019] | ✔️ | ||
HetGNN[KDD 2019] | ✔️ | ✔️ | |
HeGAN[KDD 2019] | ✔️ | ||
HGAT[EMNLP 2019] | |||
GTN[NeurIPS 2019] & fastGTN | ✔️ | ||
RSHN[ICDM 2019] | ✔️ | ✔️ | |
GATNE-T[KDD 2019] | ✔️ | ||
DMGI[AAAI 2020] | ✔️ | ||
MAGNN[WWW 2020] | ✔️ | ||
HGT[WWW 2020] | ✔️ | ||
CompGCN[ICLR 2020] | ✔️ | ✔️ | |
NSHE[IJCAI 2020] | ✔️ | ||
NARS[arxiv] | ✔️ | ||
MHNF[arxiv] | ✔️ | ||
HGSL[AAAI 2021] | ✔️ | ||
HGNN-AC[WWW 2021] | ✔️ | ||
HeCo[KDD 2021] | ✔️ | ||
SimpleHGN[KDD 2021] | ✔️ | ||
HPN[TKDE 2021] | ✔️ | ✔️ | |
RHGNN[arxiv] | ✔️ | ||
HDE[ICDM 2021] | ✔️ | ||
HetSANN[AAAI 2020] | ✔️ | ||
ieHGCN[TKDE 2021] | ✔️ |
OpenHGNN Team[GAMMA LAB], DGL Team and Peng Cheng Laboratory.
See more in CONTRIBUTING.
If you use OpenHGNN in a scientific publication, we would appreciate citations to the following paper:
@inproceedings{han2022openhgnn,
title={OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network},
author={Hui Han, Tianyu Zhao, Cheng Yang, Hongyi Zhang, Yaoqi Liu, Xiao Wang, Chuan Shi},
booktitle={CIKM},
year={2022}
}
OpenHGNN是由北邮GAMMA Lab开发的基于PyTorch和DGL的开源异质图神经网络工具包。
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